1. Field of the Invention
Near-infrared spectroscopic calibration models are developed for the rapid, clean and reliable prediction of the total dietary fiber content in a wide range of cereal products, including mixed grain products and cereal products with high sugar content, high crystal sugar content, high fat content, and high sugar and fat content.
2. Description of the Related Art
The medical and nutritional communities have long recognized the health benefits of a high fiber diet. For many years Americans have been encouraged to increase their consumption of high fiber foods such as vegetables, fruits and whole grain cereal products. To help consumers to make informed and healthful food choices, the Nutrition Labeling and Education Act (NLEA) was created in 1990. This legislation requires that the amount of total dietary fiber present in a product be included on the nutrition label (Code of Federal Regulations, FDA, HHS; 21 CFR .sctn.101.9, 1995).
The method currently in use in the United States, Canada, and many European countries for dietary fiber content analysis for nutrition labeling is the AOAC enzymatic-gravimetric total dietary fiber method (AOAC Official Methods of Analysis, 15.sup.th Ed., "Total Dietary Fiber in Foods: Method"; AOAC: Arlington, Va. 1990 and 1992). The AOAC method has been used with consistent results, over time, and on a wide variety of food products, grains, and fresh fruits and vegetables. However, it is a very time consuming "wet technique" (taking 2-3 days), expensive, and labor intensive. A more rapid method is needed to help industries comply with the NLEA and to help regulatory agencies efficiently monitor industry compliance.
Near-infrared reflectance spectroscopy (NIRS) is rapid, inexpensive, and clean. However, the amount of intelligence which can be derived from a near-infrared spectral scan is limited. In its most basic form, it is known that the near infrared absorbance spectrum of a sample of, e.g., ground grain, may be plotted to depict absorbance as a function of wavelength. The shape of the absorbance spectrum, including relative magnitudes and wavelengths of peak absorbances, may provide a characteristic "fingerprint" of certain analytes in the sample, by means of which the analytes in the sample may be quantified, particularly where the spectral peaks of the analyte in interest in the sample are unmasked and approximately proportional to the concentration of the analyte in the sample.
However, in the field of agricultural products, materials to be analyzed do not have uniform compositions. The constituent in interest may be partially or fully masked by a complex background of other components. It may also be the case that the target "analyte" is not a simple discrete element or compound, but rather a class of compounds not liable to being "fingerprinted". Thus, limitations are quickly reached as to the amount of information that can be expected to be extracted from bare spectral data.
Mathematical tools have been developed to help extract additional information from spectra. Chemometrics has been described as the application of mathematical and statistical methods to extract more useful chemical information from chemical and physical measurement data. Chemometrics has become a necessary tool, applying computerized data analysis techniques to help find deeply hidden relationships between variables among large volumes of raw data (Workman et al, Applied Spectroscopy Reviews, 31 (1&2), 73-124 (1996)). Recent advances have lead to new data analysis systems and a new breed of analytical tools--microprocessor controlled "intelligent" instrumentation. Commercially available NIRS instruments are provided with software packages and use one or more of several methods to convert NIR information to percent composition. So it is known to convert reflectance data to data representing log 1/R values, wherein R is reflectivity, which values vary approximately linearly with the concentration of the absorber. From the log 1/R data, the first or second derivative may be determined. Most methods use some linear combination of these values at a few wavelengths (.ltoreq.10). The first or second derivative values are inserted into equations in which the coefficients are determined by linear regression on known samples. The resulting measurements have been correlated to easily defined classes of chemical compounds, e.g., oil, protein, and water content of grain samples.
However, even those working in this art would not be able to predict whether NIRS could be used as a predictor for a target analyte as varied and imprecise as "total dietary fiber" in samples as varied as those which the AOAC enzymatic-gravimetric total dietary fiber method routinely analyzes. To compete with the AOAC method, any method would have to be capable of determining total dietary fiber over a wide range of grains and cereal products, even high sugar and/or high fat content products. Mixed grain cereals and cereal products containing high fat and high sugar represent a significant portion of the cereal product market and baking industries. It is well known that NIR spectral properties of cereal products containing high fat or sugar can differ substantially from the spectral properties of other cereal products. For example, the NIR spectra of sucrose, fructose, and glucose differ significantly. If NIRS can not be made to accurately and reliably predict dietary fiber content of most or all types of bakery products, regardless of fat content, sugar type and content, and grain heterogeneity, then it has little or no use as a commercial indicator.